Fatigue Monitoring Using Wearables and AI: Trends, Challenges, and Future Opportunities

Monitoring fatigue is essential for improving safety, particularly for people who work long shifts or in high-demand workplaces. The development of wearable technologies, such as fitness trackers and smartwatches, has made it possible to continuously analyze physiological signals in real-time to determine a person level of exhaustion. This has allowed for timely insights into preventing hazards associated with fatigue. This review focuses on wearable technology and artificial intelligence (AI) integration for tiredness detection, adhering to the PRISMA principles. Studies that used signal processing methods to extract pertinent aspects from physiological data, such as ECG, EMG, and EEG, among others, were analyzed as part of the systematic review process. Then, to find patterns of weariness and indicators of impending fatigue, these features were examined using machine learning and deep learning models. It was demonstrated that wearable technology and cutting-edge AI methods could accurately identify weariness through multi-modal data analysis. By merging data from several sources, information fusion techniques enhanced the precision and dependability of fatigue evaluation. Significant developments in AI-driven signal analysis were noted in the assessment, which should improve real-time fatigue monitoring while requiring less interference. Wearable solutions powered by AI and multi-source data fusion present a strong option for real-time tiredness monitoring in the workplace and other crucial environments. These developments open the door for more improvements in this field and offer useful tools for enhancing safety and reducing fatigue-related hazards.